Evaluation of Pose Only SLAM

Hu, Gibson; Huang, Shoudong; Dissanayake, Gamini

doi:10.1109/iros.2010.5649825

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Due to the better consistency and efficiency, the optimization-based methods, especially for the pose-graph SLAM framework [31], are widely applied in the indoor navigation methods of multiple kinds of the robot systems. The authors in [32] present a mobile mapping system (MMS) of which the front-end is the map-based scan matching and back-end is the graph-based optimization.…”

Section: Related Workmentioning

confidence: 99%

Submap-Based Indoor Navigation System for the Fetch Robot

Leighton

Zhu

et al. 2020

IEEE Access

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In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.

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Section: Related Workmentioning

confidence: 99%

Submap-Based Indoor Navigation System for the Fetch Robot

Leighton

Zhu

et al. 2020

IEEE Access

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“…It is well-known that SLAM technology develops from the filter-based method to the optimization-based method. Because of the better consistency and efficiency, the optimization-based method, especially for the pose-graph SLAM framework [10], is widely used in the indoor navigation method of the robot system. Yu-Cheol Lee and Seung-Hwan Park present a mobile mapping system (MMS) whose front-end is the map-based scan matching and back-end is the graph-based optimization [11].…”

Section: Related Workmentioning

confidence: 99%

Indoor Navigation System Using the Fetch Robot

Zhu

Leighton

et al. 2019

Intelligent Robotics and Applications

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In this paper, we present a navigation system, including offline mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.

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“…An evaluation of pose only SLAM algorithm as compared with feature based SLAM is performed in terms of information loss [31]. This work asks how much accuracy or consistency is compromised for the efficiency in tree-based network optimizer (TORO) [28] or pose only SLAM.…”

Section: Information Exploitationmentioning

confidence: 99%

A review of recent developments in Simultaneous Localization and Mapping

Dissanayake

Huang

Wang

et al. 2011

2011 6th International Conference on Industrial and Information Systems

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Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding of the fundamental properties of the SLAM problem and associated issues with the view to consolidate recent achievements.

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Evaluation of Pose Only SLAM

Cited by 10 publications

References 15 publications

Submap-Based Indoor Navigation System for the Fetch Robot

Submap-Based Indoor Navigation System for the Fetch Robot

Indoor Navigation System Using the Fetch Robot

A review of recent developments in Simultaneous Localization and Mapping

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